To control technical processes, what are known as PID controllers are known, these generating a signal for a manipulated variable by detecting a reference variable and a control variable. The aim of such a PID controller is to take into account internal transfer behavior when generating the manipulated variable for each technical process. In the specific case, by measuring the manipulated variable already incorporated into a technical process, the effect thereof on the control variable when this manipulated variable is incorporated further is intended to be taken into account. The PID controller firstly generates, in its P-branch (P for “proportional”), a proportion of its manipulated variable proportional to the control error (difference between reference variable and control variable), adds thereto, via its I-branch (I for “integral”), a further manipulated variable proportion by integrating the control error, and thus takes into account the effect of the past control error, and adds thereto, via its D-branch (D for “differential”), a further manipulated variable proportion that takes into account the current rate of change of the control error. Manipulated variables may be power consumptions of a technical process, and may be mixture ratios, assignment of an amount of substance and many other possible variables. The PID controller is able to control a technical process with a higher-order delay behavior such that the control variable stays as close as possible to the reference variable, even under the influence of interfering variables.
A PID controller should be adapted to the temporal behavior of a technical process on the basis of its parameters. To optimize the control parameters, it is therefore necessary to have very good knowledge about the technical process to be controlled. If accurate process knowledge is not available, it is then necessary to estimate a process model by way of the reaction behavior of the technical process, in particular the change in the control variable upon a forced change in the manipulated variable, in order in turn to estimate the individual control parameters of the PID controller therefrom. Depending on the properties of the technical process to be controlled, slight changes in the control parameters may generate a large deviation in the control behavior. To estimate control parameters, various strategies have been developed that allow the control parameters to be estimated to a satisfactory degree with acceptable time expenditure. To apply such a control strategy, however, high expertise and also a certain amount of experience are likewise necessary. Optimizing the parameterization of a controller therefore likewise requires a high degree of specialist knowledge. The theory of a controller, in particular the theory of a PID controller together with the known parameterization strategies, is assumed to be known at this juncture.
Industrial installations usually have a relatively high number of controllers. When commissioning an industrial installation, considerable efforts are made with respect to parameter optimization. As soon as the industrial installation is brought into service, experts who are entrusted with the industrial process itself take over control of the industrial installation. Since industrial installations are subject to certain fluctuations over the long term, be this through a variation in the raw materials, through a change in machine powers or through continuous optimization of production or manufacturing processes, the control parameters of an industrial installation also drift over time. Renewed parameterization again requires a high degree of time and expertise. The reason for the required expertise and time expenditure is that the control parameters have to be adapted to the changed process behavior. However, the settable control parameters do not have a direct relationship with the control behavior of a controller, such as the damping behavior and the temporal behavior of the control procedure.